esm – skill Lập trình cho agent AI

ESM: protein generation, embeddings, and folding

When to use

Use for Biohub's esm SDK: ESM3 masked multimodal generation, ESMC sequence

representations, and ESMFold2 all-atom prediction. The former Forge platform

migrated to https://biohub.ai, including ESM3; SDK class names still contain

Forge. This skill targets the released esm 3.4.1.post1, verified against

its wheel and current official documentation.

fair-esm is Meta's separate, older ESM2/ESMFold/ESM-IF distribution. Both

packages import as esm; install them in separate environments. The legacy

esm.pretrained.esm2_* interface is not the Biohub ESMC interface. Hugging Face

Transformers' native ESMC implementation is another API: do not interchange its

version requirements or output types with this SDK.

Setup and model choice

uv venv --python 3.12 .venv-esm
uv pip install --python .venv-esm/bin/python "esm==3.4.1.post1"

The release declares Python >=3.12, Torch >=2.11,<2.12 and Transformers

>=4.57.6,<5. Python 3.12, Torch 2.11.0 and Transformers 4.57.6 were tested on

CPU. Linux x86_64 installs include GPU-specific dependencies; use a supported

inference machine and budget disk space. Flash Attention is optional; it is

unnecessary for the tiny CPU tests. Do not install this stack into a shared

environment containing incompatible Transformers or Torch pins.

| Task | Local model/API | Hosted client/model |

| --- | --- | --- |

| ESM3 sequence/structure/function generation | ESM3.from_pretrained("esm3-sm-open-v1") | client("esm3-medium-2024-08"); small/large IDs in the ESM3 reference |

| ESMC embeddings | EsmcForMaskedLM and EsmcTokenizer; biohub/ESMC-300M, biohub/ESMC-600M, biohub/ESMC-6B | esmc_client("esmc-600m-2024-12"); 300M/6B also documented |

| ESMFold2 all-atom structures | EsmFold2Model, ESMFold2InputBuilder; biohub/ESMFold2 | esmfold2_client("esmfold2-fast-2026-05") |

ESMC 6B weights are now available locally. Current Biohub model cards identify

MIT licensing, with ESMC cards also linking third-party notices; review the

exact selected artifact's card and access requirements.

The older ESMC class remains as a deprecated compatibility wrapper. Local

model sizes, hosted availability and account quotas are different constraints;

a larger model does not guarantee better performance on a particular assay.

Workflow

1. Specify the sequence/chain/residue mapping and whether the objective is

generation, representation extraction or folding. Preserve input IDs and

source provenance. Reject accidental ..., spaces, FASTA headers or gaps

in plain single-chain sequences rather than silently deleting them.

2. Choose a local checkpoint or explicit hosted model ID. Record SDK, checkpoint

revision, model settings, mask locations and random seed where supported.

3. Check every SDK result for ESMProteinError. Hosted failures may be returned

as values. Use finite request timeouts, context managers and bounded

concurrency. See [hosted contracts](references/forge-api.md).

4. Validate the output contract: sequence length and fixed residues, residue-only

embedding axes, or chain/atom mapping and confidence. Keep model predictions

separate from experimental validation.

5. Save sequences/structures with settings and identifiers. Avoid caches keyed

only by sequence when model, structure, function or generation settings differ.

ESM3 completion and fresh structure prediction

Illustrative pretrained inference; no weights or hosted jobs were run in this

refresh. This toy sequence demonstrates API mechanics, not a functional design.

import torch
from esm.models.esm3 import ESM3
from esm.sdk.api import ESMProtein, ESMProteinError, GenerationConfig

model = ESM3.from_pretrained("esm3-sm-open-v1", device=torch.device("cpu"))
prompt = "MPRT___KEND"
completed = model.generate(
    ESMProtein(sequence=prompt),
    GenerationConfig(track="sequence", num_steps=3, temperature=0.7),
)
if isinstance(completed, ESMProteinError):
    raise completed
assert completed.sequence is not None and len(completed.sequence) == len(prompt)
assert "_" not in completed.sequence
assert all(a == "_" or a == b for a, b in zip(prompt, completed.sequence))

# A fresh sequence-only prompt prevents old coordinates conditioning the check.
folded = model.generate(
    ESMProtein(sequence=completed.sequence),
    GenerationConfig(track="structure", num_steps=8),
)
if isinstance(folded, ESMProteinError):
    raise folded
assert folded.coordinates is not None
folded.to_pdb("candidate.pdb")

Generation fills masked positions. Calling it again on a completed track does

not implement refinement or temperature annealing; explicitly remask selected

positions or clear the track. ESM3 structure generation and ESMFold2 prediction

use different models and result types. See [ESM3](references/esm3-api.md) for

inverse folding, function vocabulary and coordinate conventions.

ESMC embeddings with correct residue pooling

Illustrative pretrained loading; the same API and pooling were executed with a

tiny randomly initialized model on CPU. Add this skill's scripts/ directory to

PYTHONPATH when importing the bundled helper.

from esm.models.esmc import EsmcForMaskedLM, EsmcTokenizer
from esm_embeddings import embed_sequences

model = EsmcForMaskedLM.from_pretrained("biohub/ESMC-300M", device="cpu").eval()
tokenizer = EsmcTokenizer()
sequences = ["MPRTKEINDAGLIVHSPQWFYK", "ACDEFGHIK"]
features = embed_sequences(model, tokenizer, sequences)
assert features.shape == (2, 960)

output.last_hidden_state has shape (B,T,D) and includes CLS, EOS and padding;

T is not the raw residue count. [scripts/esm_embeddings.py](scripts/esm_embeddings.py)

performs one real padded batch, excludes special/padding tokens, validates the

residue count, and returns (B,D) CPU features in input order. Choose batch size

by sequence lengths and available memory. It does not truncate, download weights

or contact a service. See [ESMC](references/esm-c-api.md) for hosted output types,

per-residue extraction, gradient behavior and migration details.

Hosted authentication and folding

Read only the intended credential from the environment; pass it explicitly so

it is resolved when the client is created. SDK factory defaults capture

ESM_API_KEY at import time. Create keys in the

[Biohub developer console](https://biohub.ai/developer-console/api-keys).

Illustrative authenticated inference:

import os
from esm.sdk import esmfold2_client
from esm.sdk.api import ESMProteinError, FoldingConfig
from esm.utils.structure.input_builder import ProteinInput, StructurePredictionInput

fold_input = StructurePredictionInput(
    sequences=[ProteinInput(id="A", sequence="MPRTKEINDAGLIVHSPQWFYK")]
)
with esmfold2_client(
    model="esmfold2-fast-2026-05", url="https://biohub.ai",
    token=os.environ["ESM_API_KEY"], request_timeout=300,
) as client:
    result = client.fold_all_atom(fold_input, config=FoldingConfig())
if isinstance(result, ESMProteinError):
    raise result
with open("candidate.cif", "w") as handle:
    handle.write(result.complex.to_mmcif())

Use the [Biohub/ESMFold2 reference](references/biohub-platform.md) for MSA,

confidence and complex-input conventions. The fast hosted model ignores MSAs.

Public source and mocked requests validate the client contract; they do not

establish account access, service availability or prediction quality.

Scientific checks

• Function annotations use supported tokenizer labels and 1-based inclusive

ranges; arbitrary labels such as enzymatic_activity are not valid prompts.

• ESM3 coordinates are tensors in atom37 layout; missing atoms use NaN. Tensor

copies use .clone(). Specify a PDB chain instead of assuming the default

selects one chain; the current default is chain_id="all".

• Predicted coordinates, pLDDT and pTM do not measure thermodynamic stability,

binding affinity or catalytic activity. Inverse-folded sequences need fresh

prediction, matched-residue structural comparison and experimental screening.

• Embedding similarity is not a homology/function guarantee. Evaluate supervised

models with homology-aware splits; fit normalization and dimensionality

reduction on training data. Report uncertainty and independent holdout metrics.

• See [worked workflows](references/workflows.md) for variant libraries,

structure-conditioned design, and clustering without unsupported stability

scores or fixed PCA/t-SNE settings that fail on tiny datasets.

Verification and sources

[references/review.md](references/review.md) records official sources, executed

CPU tests and limitations. Run python tests/run_all.py --isolated esm from the

repository to exercise synthetic local models, pooling, input serialization,

PDB round trips and mocked hosted contracts. Pretrained ESM3/ESMC/ESMFold2,

CUDA inference and authenticated services were not executed.

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